Neural Network

CMT: Convolutional Neural Networks Meet Vision Transformers

[arxiv] 1. Introduction This repo is the CMT model which impelement with pytorch, no reference source code so this is a non-official version. 2. Enveriments python 3.7+ pytorch 1.7.1 pillow apex opencv-python You can see this repo to find how to install the apex 3. DataSet Trainig /data/home/imagenet/train/xxx.jpeg, 0 /data/home/imagenet/train/xxx.jpeg, 1 … /data/home/imagenet/train/xxx.jpeg, 999 Testing /data/home/imagenet/test/xxx.jpeg, 0 /data/home/imagenet/test/xxx.jpeg, 1 … /data/home/imagenet/test/xxx.jpeg, 999 4. Training & Inference

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Neural Network

One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing

Unofficial pytorch implementation of paper “One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing” Driving | FOMM | Ours: Free-View: Train: python run.py –config config/vox-256.yaml –device_ids 0,1,2,3,4,5,6,7 Demo: python demo.py –config config/vox-256.yaml –checkpoint path/to/checkpoint –source_image path/to/source –driving_video path/to/driving –relative –adapt_scale –find_best_frame free-view (e.g. yaw=20, pitch=roll=0): python demo.py –config config/vox-256.yaml –checkpoint    

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Transformer

nnFormer: Interleaved Transformer for Volumetric Segmentation

Code for paper “nnFormer: Interleaved Transformer for Volumetric Segmentation “. Please read our preprint at the following link: paper_address. Parts of codes are borrowed from nn-UNet. Installation 1、System requirements This software was originally designed and run on a system running Ubuntu 18.01, with Python 3.6, PyTorch 1.8.1, and CUDA 10.1. For a full list of software packages and version numbers, see the Conda environment file environment.yml. This software leverages graphical processing units (GPUs) to accelerate neural network training and evaluation; […]

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Camera

CTRL-C: Camera calibration TRansformer with Line-Classification

This repository contains the official code and pretrained models for CTRL-C (Camera calibration TRansformer with Line-Classification). Jinwoo Lee, Hyunsung Go, Hyunjoon Lee, Sunghyun Cho, Minhyuk Sung and Junho Kim. ICCV 2021. Single image camera calibration is the task of estimating the camera parameters from a single input image, such as the vanishing points, focal length, and horizon line. In this work, we propose Camera calibration TRansformer with Line-Classification (CTRL-C), an end-to-end neural network-based approach to single image camera calibration, which […]

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Images

The Intrinsic Dimension of Images and Its Impact on Learning

Estimating the instrinsic dimensionality of image datasets Code for: The Intrinsic Dimensionaity of Images and Its Impact On Learning – Phillip Pope and Chen Zhu, Ahmed Abdelkader, Micah Goldblum, Tom Goldstein (ICLR 2021, spotlight) Environment This code was developed in the following environment conda create dimensions python=3.6 jupyter matplotlib scikit-learn pytorch==1.5.0 torchvision cudatoolkit=10.2 -c pytorch To generate new data of controlled dimensionality with GANs, you must install: pip install pytorch-pretrained-biggan To use the shortest-path method (Granata and Carnevale 2016) you […]

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Pytest

A Django plugin for pytest

Welcome to pytest-django! pytest-django allows you to test your Django project/applications with the pytest testing tool. Install pytest-django pip install pytest-django Why would I use this instead of Django’s manage.py test command? Running your test suite with pytest-django allows you to tap into the features that are already present in pytest. Here are some advantages: Manage test dependencies with pytest fixtures. Less boilerplate tests: no need to import unittest, create a subclass with methods. Write tests as regular functions. Database […]

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Testing

splinter: an open source tool for testing web applications using Python

splinter – python tool for testing web applications splinter is an open source tool for testing web applications using Python. It lets you automate browser actions, such as visiting URLs and interacting with their items. Sample code from splinter import Browser browser = Browser() browser.visit(‘http://google.com’) browser.fill(‘q’, ‘splinter – python acceptance testing for web applications’) browser.find_by_name(‘btnK’).click() if browser.is_text_present(‘splinter.readthedocs.io’): print(“Yes, the official website was found!”) else: print(“No, it wasn’t found… We need to improve our SEO techniques”) browser.quit() Note:    

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Django

A live profiling and inspection tool for the Django framework

Silk is a live profiling and inspection tool for the Django framework. Silk intercepts and stores HTTP requests and database queries before presenting them in a user interface for further inspection: SECURITY NOTE: Because Silk stores all HTTP requests into the database in plain text, it will store the request’s sensitive information into the database in plain text (e.g. users’ passwords!). This is a massive security concern. An issue has been created for this here. Contents Requirements Silk    

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Testing

Web testing library for Robot Framework

SeleniumLibrary is a web testing library for Robot Framework that utilizes the Selenium tool internally. The project is hosted on GitHub and downloads can be found from PyPI. SeleniumLibrary works with Selenium 3 and 4. It supports Python 3.6 or newer. In addition to the normal Python interpreter, it works also with PyPy. SeleniumLibrary is based on the old SeleniumLibrary that was forked to Selenium2Library and then later renamed back to SeleniumLibrary. See the Versions and History sections below for […]

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Models

Do Prompt-Based Models Really Understand the Meaning of Their Prompts?

This repository accompanies our paper “Do Prompt-Based Models Really Understand the Meaning of Their Prompts?” Usage To replicate our results in Section 4, run: python3 prompt_tune.py –save-dir ../runs/prompt_tuned_sec4/ –prompt-path ../data/binary_NLI_prompts.csv –experiment-name sec4 –few-shots 3,5,10,20,30,50,100,250 –production –seeds 1 Add –fully-train if you want to train on the entire training set in addition to few-shot settings. To replicate Section 5, run: python3 prompt_tune.py –save-dir ../runs/prompt_tuned_sec5/ –prompt-path ../data/binary_NLI_prompts_permuted.csv    

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